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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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A mining Frequent Itemsets Algorithm in Stream data Based on Sliding Time Decay Window 20

A Mining Frequent Itemsets Algorithm in Stream Data Based on...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Lu, Xin Jin, Shaonan Wang, Xun Yuan, Jiao Fu, Kun Yang, Ke Univ Elect Sci & Technol China Sch Software Chengdu Peoples R China China Railway Eryuan Engn Grp Co Ltd Chengdu Peoples R China

ISBN: (纸本)9781450375511

In order to reduce the time and memory consumption of frequent itemsets mining in stream data, and weaken the impact of historical transactions on data patterns, this paper proposes a frequent itemsets mining algorithm SWFIUT-stream based on sliding decay time window. In this algorithm, the time attenuation factor is introduced to assign different weights to each window unit to weaken their influence on data mode. In order to realize the fast stream data mining processing, when mining the frequent itemsets, the two-dimensional table is used to scan and decompose the itemsets synchronously to mine all the frequent itemsets in the window, and the distributed parallel computing processing is carried out based on storm framework. Experimental data show that the algorithm consumes less time and consumes less memory space than conventional algorithms when mining frequent itemsets in stream data.

关键词： Sliding window stream data frequent itemset storm framework

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A Non-intrusive Load recognition Method Based on Convolutional Feature Fusion

A Non-intrusive Load Recognition Method Based on Convolution...

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Digital Society and Intelligent Systems (DSInS), international conference on

作者： Wenbo Zhu Weiyuan Huang Gaoyan Cai Jianwen Chen Zhongbo Zhang Haibing Li Wei Yu School of Mechatronic Engineering and Automation Foshan University Foshan China R&D Dept Hodi Technologies Co. Ltd Foshan China

ISBN: (数字)9798350331387

ISBN: (纸本)9798350331394

In non-intrusive load recognition, the aggregated load can lead to the feature information of individual target appliance being obscured, which then decreases the recognition accuracy. Although the existing methods can, to some extent, improve the accuracy of appliance identification under aggregated loads, most of them only work on identification under single modal information and do not focus on learning of information between different modalities of the load. Therefore, a cross-modal and multi-scale load convolution feature fusion recognition method was proposed in this paper. Firstly, the feature information at different scales among the three modalities of appliances was extracted by multi-layer convolution. Considering the differences in feature distributions between sequences and images, the use of traditional feature concatenation methods will lead to large deviations in feature ratio, thus reducing the ability of the model to learn the features of different modalities. To this end, a feature reconstruction scheme for balancing the feature information ratio among different modalities was proposed to improve this problem. Then, the multi-scale residuals were used to progressively fuse features at different scales among the modalities to achieve feature learning for the three modalities of appliances. After evaluation on a real datasets, the experimental results showed that the method proposed in this paper has a better performance. Compared with multiple neural network-based image classifier methods, this method has higher accuracy in recognition under aggregated load.

关键词： Time-frequency analysis Correlation Target recognition Convolution Feature extraction data models data mining

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Facial Expression recognition via ResNet-18 1

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3rd EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2021

作者： Li, Bin Li, Runda Lima, Dimas School of Computer Science and Technology Henan Polytechnic University Jiaozuo Henan454000 China Nanjing Foreign Language School Nanjing Jiangsu China Department of Electrical Engineering Federal University of Santa Catarina Florianópolis Brazil

ISBN: (数字)9783030825652

ISBN: (纸本)9783030825645

As an important part of human-computer interaction, facial expression recognition has become a hot research topic in the fields of computer vision, pattern recognition, artificial intelligence, etc., and plays an important role in our daily life. With the development of deep learning and convolutional neural network, the research of facial expression recognition has also made great progress. Moreover, in the current face emotion recognition research, there are problems such as poor generalization ability of network model. The extraction of traditional facial expression recognition features is complex and the effect is not ideal. In order to improve the effect of facial expression recognition, we propose a feature extraction method for deep residual network, and use deep residual network ResNet-18 to extract the features of the data set. Through the experimental simulation of the specified data set, it can be proved that this model is superior to state-of-the-art methods model. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep learning

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An Approach to Ensemble Simplified ResNet Models-Using Egyptian Hieroglyphs data Set

An Approach to Ensemble Simplified ResNet Models-Using Egypt...

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Zixiang Yan Kecheng Jin Jingkun Hou Pu Zhang College of Computer and Information Engineering (College of Artificial Intelligence) Nanjing Tech University Nanjing China Nanjing Hankai Academy Nanjing China School of Inteligence Science and Technology University of Science and Technology Beijing Beijing China Austin Friars St Monica's School Etterby Scaur Carlisle Cumbria UK

Since the inception of computer vision, recognizing text, including both printed and handwritten characters, has posed a significant challenge for traditional machine learning models. However, only a handful of these models have focused on ideographical scripts. Similarly, while mathematical solutions to common neural network problems such as degradation and computational complexity have been studied, possible shortcuts are often overlooked. This paper presents an innovative approach by introducing a novel ensemble network called "SparrowNet" and compares it to ResNet18 and GoogLeNet V2. Using an open-source Egyptian hieroglyph dataset, the paper focuses on evaluating the efficiency and accuracy of different networks using various performance metrics. Additionally, the paper discusses the advantages of the two existing models and explains the reasons behind the modifications made to SparrowNet. Through this research, it has been demonstrated that the use of 1x1 convolutions can significantly improve speed and reduce computational complexity, while a voting mechanism can be a novel solution to prevent degradation, and the modified network is capable to solve this particular character recognition task.

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3rd international conference on Computing and data Science, CONF-CDS 2021

3rd International Conference on Computing and Data Science, ...

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3rd international conference on Computing and data Science, CONF-CDS 2021

ISBN: (纸本)9789811688843

The proceedings contain 31 papers. The special focus in this conference is on Computing and data Science. The topics include: Application of Graphical Convolutional Networks to the Safety Assessment of Container Ship Voyages;evaluation of Quantization Techniques for Deep Neural Networks;multi-channel Relation Modeling for Session-Based Recommendation with Self-supervised learning;Polyp Segmentation Using Fully Convolutional Neural Network with Dropout and CBAM;Evaluation of the Effectiveness of COVID-19 Prevention and Control Based on Modified SEIR Model;finite Difference Method for Convection-Diffusion Equation;A Review of Bayesian Posterior Distribution Based on MCMC Methods;roboTutor: Predictions of Students’ Answer Type;PM2.5 Density Prediction Based on a Two-Stage Rolling Forecast Model Using LightGBM;stock Market Movement Prediction: A Comparative Study Between machine learning and Deep Time Series Models;a Spatial-Temporal Adaptive Video Denoising Algorithm;you Are What You Eat: Predictive Model of Eating Habits and Health Outcomes;using Co-evolution of Artefacts in Git Repository to Establish Test-to-Code Traceability Links on Method-Level;convergence Media Related Information Production Platform Based on Kalman Filter Video Target Tracking;Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation;statistics and machine learning for Behavioral Prediction of Operational Transconductance Amplifiers with Focus on Regression Analysis;comparative Study of Loss Function Based on Neural Network;Research on the Prediction of Logistics Demand for Emergencies Based on BP Neural Network;research on Twitter data Crawling and data Visualization Analysis Based on Python;the Survey of Joint Entity and Relation Extraction;occluded Face recognition with Deep learning.

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The RSDS: A Current State and Future Plans

The RSDS: A Current State and Future Plans

引用

international Joint conference on Rough Sets (IJCRS)

作者： Suraj, Zbigniew Grochowalski, Piotr Rzeszow Univ Inst Comp Sci Rzeszow Rzeszow Poland

ISBN: (纸本)9783030873349;9783030873332

This paper provides a brief overview of the Rough Set database System (the RSDS for short) for creating bibliographies on rough sets and related fields, as well as sharing and analysis. The current version of the RSDS includes a number of modifications, extensions and functional improvements compared to the previous versions of this system. The system was made in the client-server technology. Currently, the RSDS contains over 38 540 entries from nearly 42 860 authors. This system works on any computer connected to the Internet and is available at http://***.

关键词： Rough set Soft computing Ontology data mining Knowledge discovery pattern recognition machine learning database system

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Odia character recognition system: A study on feature extraction and classification techniques 3

Odia character recognition system: A study on feature extrac...

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3rd international conference on Science and Engineering of Materials (ICSEM)

作者： Singh, Sunny Sarangi, Pradeepta Kumar Singla, Chaitanya Sahoo, Ashok Kumar Chitkara Univ Inst Engn & Technol Chitkara Punjab India Era Hill Univ Dept Comp Sci Graph Dehra Dun Uttarakhand India

Optical Character recognition (OCR) is the most successful application for automatic pattern recognition. OCR has been a widely studied field of research and development for more than last 50 years. It is one of the many important gifts given by computer science to the mankind. OCR means "a technique of recognition of machine printed or hand written text by computer and its conversion to an editable form as per the requirement". During the recent decades, handwritten character recognition has been a subject of study because of both its theoretical importance in recognition system with numerous possible applications. This paper presents an extensive study of numerous publications by a number of researchers working for years in the field of Odia character recognition (one of the many regional languages used in India and spoken by more than 30 million people worldwide). The main focus has been on feature extraction, data set size, classification techniques and recognition accuracy. A number of selective articles published in journals and conference proceedings on character recognition have been included in this work (2005 to 2020). The review process has been divided into two parts. In the first part a study of various works by researchers is presented with detailed description. In the second part, various feature extraction and classification techniques have been presented and summarized. The findings are presented in the analysis section. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Handwritten Odia numerals Character recognition Feature extraction Classification techniques pattern recognition

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Review on the Challenges and Future Directions of Deep learning-Based Techniques for Advance Prediction of Cardiac Attack 1

引用

3rd international conference on machine Intelligence and Smart Systems, MISS 2023

作者： Kumar, Shrawan Thakur, Bharti Yogananda School of AI Computer and Data Sciences Shoolini University H.P. Solan India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

One of the challenging medical areas that makes it difficult to predict an event before it occurs is a cardiac attack or syndrome. Insufficient oxygen intake will result in improper electric heart rhythm balance, changes in irregular heartbeats over time, and imbalance in all bodily functions throughout the day, which will ultimately cause an attack of heart valves and blood flow. Since the disease has a very slow response time, early-stage prediction is necessary. The main goal of the study is to create an expert system that uses multi-variate feature predictors and Deep learning (DL) classification algorithms to handle independent and multi-class variables to predict the incidence of a cardiac attack for a patient at the current stage or in the future. The research aims to create a framework model using cutting-edge algorithmic techniques that will motivate medical professionals to find early warning signs of cardiac attack so they can find ways to save patients. Around fifty reviews are essential for designing the architecture of the expert system design for the prediction of a cardiac attack according to a thorough literature re- view on different algorithms and their effectiveness in predicting cardiac attacks. This knowledge review proposes may lead to addressing early cardiac attacks that accept the selected features from different feature predictors and forms a fuzzification of selected features along with rule sets and knowledge-base from experts using the DL and data mining algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Expert systems

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Research and Implementation of Image recognition FOR Certificate Sortingand Stamping Robot

Research and Implementation of Image Recognition FOR Certifi...

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international conference on Applied machine learning (ICAML)

作者： Yujun Shan Na Liu Ming Yang Liaoning Inspection Examination & Certification Centre Shenyang China

ISBN: (纸本)9781665421263

This paper designs a certificate sorting and sealing robot, and it introduces a method of collecting and processing certificate image. By analyzing the distribution of three basic colors of its pixels, it realizes the fast location and extraction of effective information. Thus, the order and action of sorting, stamping, certificate checking and issuing are issued to the lower manipulator of the certificate sorting and stamping robot.

关键词： Image recognition Image color analysis Seals machine learning Manipulators Mathematical models data mining

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Coarse-To-Fine Key Frame Selection for Dynamic Facial Expression recognition

Coarse-To-Fine Key Frame Selection for Dynamic Facial Expres...

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Networking Systems of AI (INSAI), international conference on

作者： Shaoqi Yan Yan Wang Xinji Mai Qing Zhao Boyang Wang Wei He Wenqiang Zhang Academy for Engineering & Technology Fudan University Shanghai China Academy for Engineering & Technology School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

In studies of in-the-wild Dynamic Facial Expression recognition (DFER), most studies have focused on exploring spatial-temporal features, but neglects expression peak frame (key frame) in video sequence, which is crucial for accurate expression recognition. These key frames, capturing the most intense moments of an expression, are essential for understanding the full context and nuances of facial expressions in a video. In this paper, we propose a dynamic facial expression recognition method called C2F-KFS, which extracts key frames from video sequences by extracting features from coarse to fine, in order to improve the robustness of classification results. We designed the coarse-level spatial key frames extraction (CSKFE) module to extract spatial features from different facial regions. In addition, the fine-level temporal key frames extraction (FTKFE) module to extract feature from temporal perspective and make weighted comparison, in order to select key frames. By extensive experimental on two popular in-the-wild datasets, DFEW and FERV39k, the results and visualization show that the C2F-KFS method achieves state-of-the-arts DFER. Meanwhile, we have conducted ablation experiments to prove the effectiveness of C2FKFS method.

关键词： Representation learning Visualization Accuracy Face recognition Video sequences Feature extraction Robustness data mining Artificial intelligence

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